Verbal autopsy models in determining causes of death

Abstract

Objectives: To systematically review current practices, strengths and limitations of existing VA approaches to increase understanding of health system stakeholders and researchers.

Methods: The review was conducted and reported based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, in which articles were systematically obtained from the PubMed and SCOPUS online databases. The search was limited to English language journal articles published between 2010 and 2020. The review identified 5602 articles and after thorough scrutiny, 25 articles related to VA approaches were included.

Results: (1) InterVA and Tariff are widely used VA models; (2) Bayes rule is the most common and successful algorithm; (3) the lack of standardised datasets and metrics to evaluate models creates bias in determining VA model performance; (4) performance of the models trained using in-hospital data cannot be replicated in community death; (5) the performance of models among physicians and computer-coded algorithms differs with variation in settings.

Conclusion: The physician-certified verbal autopsy (PCVA) approaches are more effective in determining community CoD while computerised coding of verbal autopsy (CCVA) models perform well when the underlying CoD are reliably established using hospital data where data are trained in a similar environment to the target population. Our study recommends the use of hybrid models that combine strengths from various models and using an open standards dataset that includes death from different settings.

Keywords: InSilicoVA; InterVA; King and Lu; Naive Bayes; VA Algorithm; VA Models; causes of death; tariff; verbal autopsy; verbal autopsy algorithm; verbal autopsy model.